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Machine learning applied to asteroid dynamics

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Abstract

Machine learning (ML) is the branch of computer science that studies computer algorithms that can learn from data. It is mainly divided into supervised learning, where the computer is presented with examples of entries, and the goal is to learn a general rule that maps inputs to outputs, and unsupervised learning, where no label is provided to the learning algorithm, leaving it alone to find structures. Deep learning is a branch of machine learning based on numerous layers of artificial neural networks, which are computing systems inspired by the biological neural networks that constitute animal brains. In asteroid dynamics, machine learning methods have been recently used to identify members of asteroid families, small bodies images in astronomical fields, and to identify resonant arguments images of asteroids in three-body resonances, among other applications. Here, we will conduct a full review of available literature in the field and classify it in terms of metrics recently used by other authors to assess the state of the art of applications of machine learning in other astronomical subfields. For comparison, applications of machine learning to Solar System bodies, a larger area that includes imaging and spectrophotometry of small bodies, have already reached a state classified as progressing. Research communities and methodologies are more established, and the use of ML led to the discovery of new celestial objects or features, or new insights in the area. ML applied to asteroid dynamics, however, is still in the emerging phase, with smaller groups, methodologies still not well-established, and fewer papers producing discoveries or insights. Large observational surveys, like those conducted at the Zwicky Transient Facility or at the Vera C. Rubin Observatory, will produce in the next years very substantial datasets of orbital and physical properties for asteroids. Applications of ML for clustering, image identification, and anomaly detection, among others, are currently being developed and are expected of being of great help in the next few years.

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Acknowledgements

We are grateful to two anonymous reviewers for helpful and constructive comments that much increased the quality of this work. We would like to thank the Brazilian National Research Council (CNPq, grant 304168/2021-1), the São Paulo Research Foundation (FAPESP, grant 2016/024561-0), and the Institutional Training program (PCI/INPE, subproject 6.8.1, Public Call 01/2021). We are grateful to Dr. E. Smirnov and Dr. D. Duev for allowing us to use figures from their papers (Smirnov and Markov 2017; Duev et al. 2021), and to Dr. E. Smirnov for reading a preliminary version of this paper and for useful comments and suggestions. VC and RC are part of “Grupo de Dinâmica Orbital & Planetologia (GDOP)” (Research Group in Orbital Dynamics and Planetology) at UNESP, campus of Guaratinguetá. This is a publication from the MASB (Machine-learning applied to small bodies, https://valeriocarruba.github.io/Site-MASB/) research group. Questions on this paper can also be sent to the group email address: mlasb2021@gmail.com

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Authors and Affiliations

  1. São Paulo State University (UNESP), School of Natural Sciences and Engineering, Guaratinguetá, SP, 12516-410, Brazil

    V. Carruba & W. Barletta

  2. National Space Research Institute (INPE), Division of Space Mechanics and Control, C.P. 515, São José dos Campos, SP, 12227-310, Brazil

    S. Aljbaae

  3. São Paulo State University (UNESP), Sao João da Boa Vista, SP, 13876-750, Brazil

    R. C. Domingos

  4. Universidad tecnológica del Perú (UTP), Cercado de Lima, 15046, Peru

    M. Huaman

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All authors contributed to the study conception and design. Material preparation and data collection were performed by Valerio Carruba, Safwan Aljbaae and Rita Cassia Domingos. The first draft of the manuscript was written by Valerio Carruba and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Appendix 1: data on current applications of ML in asteroid dynamics

Appendix 1: data on current applications of ML in asteroid dynamics

Table 2 Reference data used to classify the status of application of machine learning to recent literature
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In this section we report the data used to classify current literature according to Fluke and Jacobs (2020) category scheme, as discussed in Sect. 4. Our data are shown in Table 2.

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Carruba, V., Aljbaae, S., Domingos, R.C. et al. Machine learning applied to asteroid dynamics. Celest Mech Dyn Astron 134, 36 (2022). https://doi.org/10.1007/s10569-022-10088-2

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